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Hello Welcome everyone. This is Dr. Shiva Ayyadurai.
For episode three of our podcast series. We have two awesome guests joining us today we have Marcelo Guadiana, who’s a student has just graduated from UMass right. Marcela, yeah, just graduated this one, this month Marcela graduated in economics and political science.
And we also have Richard Giorgio, who’s a resident in Cambridge. Richard’s been in Cambridge for how many years? Or what he what he is 40 years he so your your home is right where MIT is, right? Yes. So Richard seen sort of the growth of MIT over the years.
So that part of the military industrial complex, essentially taken over the neighborhoods of Cambridge, including Harvard, so we’ll, we’ll talk about that. But today’s talk is really about artificial intelligence, AI, which is the acronym, many of you have heard about this thing called AI, but may not fully understand what it is, except probably thinking, there’s gonna be a bunch of machines, which are going to take over your lives, and we’re gonna have robots walking around. And, and that’s sort of, you know, the idea that you have our two D TOS running around, or the little robot that vacuum cleans for you.
But today, we’re gonna really get into different aspects of AI, you know, sort of the popular idea of what AI is the much more technical knowledge what AI is, you know, I’ve been involved in AI research since the 80s. You know, since I came to MIT, it’s been one of my passions, I built a very large company out of it called Echo mail, where we used to do AI, for email analytics made a ton of money doing that. So it’s an area I know a lot about.
And it’s an area that I want to be able to share with you for various reasons. Because I believe that the mainstream media as usual, we’ll always distill something to something sort of useless, that you may not really understand. The experts will try to mislead you into whatever the flavor of the day is.
But what we we want to do on this podcast is to really give you the foundations of AI and artificial intelligence. All right, before we go on, I want to invite each of you to come to our next open house for truth, Freedom health, I personally host the open house every Thursday at 11am PST and then again at 8pm PST, you’ll learn about truth for to health, which is a movement, a platform as well as a community and much more. And we are dedicated to raising your consciousness through education theory and action practice, you will learn how to think beyond left and right Pro and anti so you may start to see things as they truly are and become a force for real and lasting change in your community.
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com/orientation be the light. Now let’s go back to our program. What we want to do on this podcast is to really give you the foundations of AI and artificial intelligence.
So that’s what I want to talk about. And obviously Marcelo, and Richard, you know, you guys are representative, many ways of sort of popular culture. So you know, you guys feel free to step in and jump in on any of these questions inside, I’ll go deeper.
But the field of AI is really about a fundamentally, how do you take human intelligence and replicate it using machines? That’s what it’s really about. So. So some very interesting terms, I just used human intelligence, the primary word there is intelligence.
In podcast two, we talked about intelligence from a systems perspective. So one of the things we really need to do is define what is intelligence? You know, before we talked about human intent, intelligence, or artificial intelligence, and we’ll get into some very deep issues of it. What is the difference between a human and a machine? And even more deeper? Aren’t we all machines? So are we what is it between artificial intelligence and human intelligence, we’ll get into this, but fundamentally think about a quote unquote, intelligent activity a human does, and being able to replicate that with the machine.
Right? So if you want to go to some very simple ideas of this, you could think about a calculator as a form of artificial intelligence, right? When I was growing up in India, my great grandfather could do, you know, fractional multiplication in his head, you know, all different kinds of fractional multiplication because he had to know how to do that when he was farming, and you had to for commerce reasons. A lot of mathematics really came out of Commerce. We didn’t really need mathematics until we started to do commerce.
You had things like the abacus that people use for doing calculations faster. When computing came, we started developing chips, we started outsourcing some of those computations to a thing called a calculator. And so the calculator could do you know, you know, 3,225,215 times 10,315,235, you know, those kinds of things fast.
Now, we could do it by paper. But we outsource that kind of computation, which was a human intelligent activity to a machine. So you could think about a calculator as a form of a machine that was able to do those kinds of computations was a form of AI.
So what is AI from a technical perspective? And when did it really start taking off, there’s a field called cybernetics, which came out during the 20s 30s, in that period, and led by a guy called Norbert Wiener, there are many other people. If you go to MIT, and you walk down the halls, when I first came to MIT called the Infinite Corridor, there’s a picture of this guy who looks like your typical nerd professor. He has a goatee, glasses, little bit of a paunch belly and a suit.
And that was Norbert Wiener. Norbert Wiener is known as a father of cybernetics, or modern AI. And Norbert Wiener is the one who started looking at the human body, or the human being as a multi tiered system.
It’s a electrical system, we have electricity running through us, it’s a magnetic system. It’s an electro mechanical system. It’s multiple systems.
And the idea was, could you start building an engineering field, and how you start developing machines which could replicate human function, and that was the field of cybernetics, and cybernetics brought in many, many different disciplines, not only physics, but it started bringing in a whole new field called information theory, led by a guy called Claude Shannon, it also brought in the fields of systems theory, General Systems Theory, thermodynamic theory, but it was a exciting period, because it brought together many, many different fields. Because if you’re going to recreate something like the human being, you have to bring in multiple fields. And so interesting enough, after Norbert Wiener really pioneered this field, he wrote a number of essays talking about how horrible artificial intelligence and cybernetics would be for humanity.
What types of machines were they building back then in the 20s and 30s? Yeah. So they were trying to do large scale computing machines, right? They were starting to get into vision systems. So one of the early problems in artificial intelligence was vision.
So think about it this way. I’ve used this in many my examples, when I try to explain what pattern recognition is. AI has become a buzzword, but the real technology behind AI is a field called pattern analysis, or pattern recognition.
So let’s think about Richard and Marcel are out a bunch of cavemen, you know, sitting around a fire, and the snake comes at you. Okay. Well, you have two choices, run, you know, or let the snake pass by.
So how do you make that decision? It’s also a field called decision theory. In fact, MIT had a whole lab still has a lab which is just recently emerging called Laboratory for Information and Decision Systems LIDS. So the fundamental thing is how do you make a decision? Snake is coming at you, how do you make a decision? Well, human beings, over many millennia, developed a way into you know, sit quietly if a snake came, it’s not poisonous, or flee or beat the hell out of the snake.
Right? That was a process that human beings went through or they survived or they died. Right so there was something in the human brain which helped us do this well. And it probably it went something like this: A snake came at you which had a Diamond Head, right, and you thought it was a nice pet, you pet it, it bit you and you died.
Okay, so you died off and let’s say Richard’s clan continued to live because they said, wait a minute, diamond shaped head. I saw that before and it bit someone else and it caused him so that knowledge in Richard’s clan was passed on and he probably taught it to other people. So they had transfer of information from one, you know, being to another, and by the way, culture is ultimately a transfer of knowledge.
Do you think there was ever like a natural instinct to people just getting a feeling…
That’s a great question. Yeah. Thought one thing right away, for the very first time.
Right. So there, there are theories and behavioral genetics, which say some of this may be hard coded, it’s not learned. It’s hard coded.
And the more interesting thing is that the genes may self — can pass on learning. They can take — learn something and pass it on, gets into a whole nother, again, that’s another podcast. But the issue is, there was a set of knowledge and learning that Richard learned that Marcelo didn’t, let’s say, caveman, Richard versus caveman, Marcelo.
And that was passed on. So people got very good at identifying poisonous snakes, and non poisonous snakes, and they made a decision, those decisions were made, right? So and you can apply this kind of knowledge transfer, and this kind of learning patterns and passing them on to in many, many different ways. You know, it could also before foraging for food, mushrooms, some mushrooms you eat, they can kill you, other mushrooms you eat, and you die.
Right? So you could argue that the existence of human beings today was through a whole history of learning decisions, and and then using those decisions to pass on to others. And so how does all of this occur? And this was really the question of cybernetics, or pattern analysis, how does this occur? How does knowledge acquired? How do we learn learning and knowledge acquisition are some of the most important things because you have to acquire knowledge and you have to learn and you have to get better and better doing doing it. So just think about forget the word AI, just think about this area of pattern analysis, and how that took place.
So I’ve shared with you how my grandmother could observe someone’s face, and look at the patterns of that face, and figure out if the person had a liver issue, or a kidney issue, or a heart issue, etc. Well, that was a pattern analysis problems. In fact, the ancient ancient rishis of India, when they were in the woods, they wouldn’t analyze all sorts of patterns, they would look at the patterns of plants, you know, their leaf shapes, and they would, and they documented these very, very, in beautiful, you know, detail.
So they knew these sets of plants were plants that supported liver function, or kidney function, or this and they learned this over time. And they enumerated it. And eventually, they built an intuition.
And we’ll talk about that when when, when you bring up the point, Marcelo, maybe you know, right away. So people say, you know, in a repetition is a mother of skill. So there is an aspect of learning that takes place, the more you do something, it becomes intuitive.
Everyone thinks, oh, intuition is something like magic. Well, it turns out, the more you do something, your brain actually learns. And it gets faster and faster doing that.
And that was the basis of one of the tools of artificial intelligence or pattern analysis called neural networks, and we’ll talk about that. Alright, but the bottom line is, there’s a learning phenomenon that takes place that you pass on, and that determines how successful you are. So, so in that 30s, period, a lot of this was driven, frankly, by war.
Alright, to create, you know, the 40s, we’re starting to, you know, World War One and Two drove a lot of this series saying the founder was warning. The he was warning us the dangers of slavery. Yeah, so So Norbert Wiener, after he is considered a pioneer, actually warn people that we probably should not do this, because of the effect it would have on human labor, it would put lots of people out of work, and that it may not actually give us a life that we actually deserve.
There are certain things human beings enjoy doing, like physical labor, that actually could be beneficial for he wrote a set of essays on this. Did anyone take him serious? Or was he I think some people did, but you have the march of the military, industrial academic complex. So give you an example of so all sorts of people got into pattern analysis.
Or again, what we say is AI today. Some behavioral psychologists got into now one of the very famous or infamous people, I don’t really care for a guy called BF Skinner. Skinner is known as the school of behavioral psychology.
He made a lot of inroads to the military and the Navy, because he proposed the you know, the pain reward model, right. The way you train animals or people is you give them pain or you give them reward. Yeah, so when people are designing missiles to go from point A to point B remember, if you want Once you missile from point A, you want it to go exactly to point B.
Well, so. And this was the basis what are called cruise missiles. So what the military did at one point, this is before computing really took off his they designed a missile.
And inside the missile, they put a TV monitor. And the TV monitor had a line between point A and point B. Okay, so the TV monitor literally had point a point B and a line connecting point A and point B.
And they put a pigeon inside the missile. And the pigeon had an electrode conductor connected to its beak. And the pigeon was taught when and by the way, so you had the actual line where the missile shield travel, and then through a gyroscope where the missile was actually going.
And the pigeon was taught if the if the second line deviated from the first line, to heck on the monitor, which gave us feedback. So the missile would make adjustments to get back to its goal. This was basically before inertial guidance.
Alright, so basically, they were using a learning algorithm, in this case, the algorithm was a pigeon was another computer. And this is how they did before we had inertial guidance. And now we have more sophisticated stuff, right, with a cruise missile, we actually are mapping the terrain, and the missile is all automated.
And that’s a form of artificial intelligence, because it’s following a pattern. But the concept of using animals or other people to automate things faster, is the whole history of artificial intelligence. You can even think about AI as being when we went from the when the when the industrial era started, you have a manufacturing line, right? You want to produce a paperclip, right? And so one person doing it by hand, you started setting up in a manufacturing line, one person simply, you know, one set of people simply got the steel ready, right, another set of people cut it, another set of people, you know, wound it up, right, another set of people packaged it.
Now, that may not sound like AI, because you don’t see a nonhuman being being used. But in many ways that was the foundations of AI because you use carbon based creatures called human beings, but you organize them in a very systematic way to do very particular things. It’s using pattern recognition.
Well, not only pattern recognition, but but separation of labor for very particular tasks. And then as machines could do some some of those things on an assembly line, you started replacing pieces of that. Right.
So I think the general thing, I think something quite different than you’ll hear anywhere in any other podcasts are going to hear, I want to define AI not as a function of a non carbon based machine, but AI in the concept of how you take some function that human beings do, and how whether we use a human being or non human being how it is systematized. So you can get better efficiency, that’s really a much more broader definition of AI. So I would argue, when we started creating the manufacturing line, you know, having giving people very specific things to do, that was a form of AI.
But we were just using human beings to do very particular things versus the individual doing everything end to end. Yeah. Okay.
So because then once you figured out that system, then you start replacing pieces with machines, right? So think about the Ford Motor Company, or Chrysler, how they build cars, initially, you know, I guess, you know, what Henry Ford fundamentally did, you could call Henry Ford. He’s not known as a builder of the car, everyone gets this wrong. He’s known actually, as the father of manufacturing line, where he wanted to create a car that everyone could buy, but what he did was he created the manufacturing line, he systematized a process.
So people are doing pieces. And then as you see that advanced and when machines came in computing came you started replacing, so you had maybe the machine automatically paint it had vision systems, which could automatically paint then you had automatic robots which did which did the bulk, right screwing, etc. But the notion of AI is not just transferring it to a machine, but it’s about figuring out a process to do things faster and faster and faster and faster.
It just so happens, you know, we start calling an artificial and we transfer that knowledge to a non carbon based entity, you know, but you have to we have to look at it really at the history of that and that’s what cybernetics was doing. So, you know, when I saw my grandmother, look at someone’s face, and and understand their that face and be able to say that person has this issue. And then she would make a decision of what combination of herbs to give that person to get them to the path to healing.
That was pattern analysis. Now in the Indian system for 1000s of years, they wrote a book called Samudrika election, which literally had every feature in the face drawn out very precisely, if you see, you know, a following a line between the two eyes, that meant there was a liver issue, if you see, you know, certain things under the eyes, kidney issue, if you see a spot on the nose, that was typically a heart issue, right. So they, I mean, I’m just giving you the high level, but they had enumerated all these patterns.
So after a while, people studied those books, and they learned but then they got better and better and better. And it was no longer doing it by rote. They built their own intuition, because their own computing took over.
And we’ll talk about this when we talked about neural networks. Interesting enough, there is a in the Sloan Management Review, which is an MIT publication. Many years ago, I think about five or six years ago, they wrote a article on intuition, which was really looking at how to chess masters, make moves.
And if you’ve ever seen the old pictures of Bobby Fischer, where there’s like 50, people lined up, and he just goes table to table the table and he makes a move. Like he’s doing a ton of moves, and he wins all the games. Well, it turns out, the difference between a chess master and a non chess master is that chess master over time, has seen so many patterns, that it’s automatic, this pattern, make this move under this pattern, make this condition, all basically their training their their brain, they don’t even think about it really.
It’s what you call intuition because, well, they are thinking, but the thinking is firing at a level that’s beyond the actual actual thinking at a very step by step manner. Another way to think about this is think about learning to ride a bike, or learning to ski. Well, if you’ve never skied before, you’re up on that hill, and you’re learning to ski the teachers telling you stuff, you know, you know, putting your pressure on your left foot, if you want to go to the right, right foot, if you want to go to the left, when you first learned skiing, you’re wobbling, you’re going all over the place, you’re falling down.
So there’s this process of learning that’s taking place. And then you look at an expert seat skier, they’re coming down the hill at 100 miles an hour, and they’re making very subtle movements, finesse. So what’s the difference? The difference is, there is physically something that’s differently wired in the brain of the professional skier, versus the novice think about when you learn to bicycling, right? I mean, obviously, something biochemically has changed when you first learn.
And then later on, you’re just bicycling and aren’t even thinking about it, even driving as well, even driving. So William James. In fact, there’s a building, if you’re in Harvard Square called the William James building, which is named after William James is known as the father of American psychology.
So William James wrote a very famous essay called associative memory. And what he conjectured This was before neural nets came, was that if you look at when you learn something, or when you experience something that literally creates physical connections in your brain, between brain centers. So he had this notion that something was changing in the brain, and he called it associative memory.
So when you learn to bicycling, there are set of parts of your brain, which are actually instantiating, which means saving that learn knowledge in the brain, it has to be saved, or it has to be stored. That’s like building neural pathways, neural pathways, so So but he was the one who came up with this concept of associative memory. Around this time, in the 40s, they’re developed a theory called neural networks.
And the idea is people are saying, well, how does that learning occur like physically? And could we create computers which emulate, which means duplicate that kind of knowledge? So the idea was this. Imagine if you’ve looked, when you have time, you can look at what are called neurons, okay? They’re nerve cells. So you have two nerve cells.
And the nerve cells, very much like other cells have membranes, they have a nucleus, but neurons also have these things called dendrites and axons, which interconnect, okay, well, there’s a sheath called a myelin sheath, which covers, you know, the extensions of the neuron. Okay? So it’s been shown now that when you learn something, what actually grows is not the nerve cell, but it’s the cabling between nerve cells. So think about if you have a live wire in your home, you plug it in, you know, the copper wire and then there’s a plastic around it, that’s called the myelin sheath, that actually gets thicker and thicker and thicker.
It’s like when you work a muscle, it’s very interesting. We all have the same number of muscle cells when you’re born, it’s fixed for men, for example. And when you, when you actually get stronger, and you work out weights, you’re actually increasing the size of the fibers.
Okay, so turns out the myelin sheath, when you experience something is actually getting bigger. So among groups of neurons, the interconnections we talked about inner connections, and podcast two, is literally growing. It’s not the nerve cells are growing, but the interconnections between the groups of neurons actually grow and getting stronger and getting stronger.
So so the connection strength between neuron one, neuron two, let’s say there’s these two neurons hanging out in your brain of, you know, many, many neurons, billions of neurons. And these two neurons are involved in learning how to bicycle. Well, the connection between those neurons as you practice day one, practice day two, there’s actually a physical part of your brain is actually getting stronger.
It’s the connection strength. So what scientists did was they said, Well, could we do this on a computer. So they that was the beginning of what are called A and ends artificial neural networks.
And so they literally created the mathematics and the computing, where they could input something into a, I’m going to call it a black box of neurons, and an output came out. And what they would do is train these neurons. So what they could do is give you a simple example, I worked on this many years ago, by the way, I think I mentioned to you AI or pattern analysis, it’s been sort of the foundation field that I was always interested in, because I want to understand my grandmother is able to do that pattern analysis.
So when I first came to MIT, one of the earliest projects I worked on since 19. For that matter, in 1978, when I was working at the University of Medicine, Dentistry, one of the things in using quote unquote, AI or pattern analysis, was babies die in their sleep. Some of them it’s called Sudden Infant Death Syndrome.
All right. And human beings have five states of sleep. We have REM sleep, where we’re, we’re, we’re, you know, having dreams, we have transitioned sleep, we have waking state anyway, adults have five states of sleep, and you could literally map it on a over time.
If you look during a 24 hour day, you could actually plot it. Children. Babies, however, have six states of sleep.
So we were trying to predict when these babies by the way, sudden infant death syndrome is when a baby suddenly stops breathing. It’s a very scary thing for parents. And what happens if the baby stops breathing, if you shake the baby, you can resuscitate them.
So the idea was, could we predict when a baby would go into that sudden state of not breathing, which is called an apnea AAP NEA. So we had Montefiore Hospital in New York at the time, gave us 48 hours of sleep data. So we literally had minute by minute by minute, the sleep states of babies.
And we also had when that baby stopped sleeping. So I built some of the early algorithms to see if I could correlate the sleep patterns to a particular point when a baby went stopped breathing, to get like the minutes right before she will tell her what Yeah, so you look at the historical patterns of sleep patterns. And so the idea was, and we built those, so So I built a whole mathematics, where because I had all this historical data, my system could learn from that.
And I could see if I could predict a pattern, and we got some very interesting data, there were definitely certain patterns, which are correlated to the apnea. And we published that in paper. My point is that pattern analysis allowed us to make a decision.
And technologies like these could get input into a shaking crib, for example. So if you detected a sleep pattern, you automatically shake a crib, that was the action. Okay? Is that what they do nowadays? Well, there’s various that’s one of the methodologies you can do, right, or you can set off an alarm.
My point is that that was an example this was when I was 14, before I built the first email system. That’s what I was working on. But it was using mathematics to look at a pattern.
Now when you look at these patterns, what you’re trying to do is you’re trying to find out is a recurring pattern. And the math which I can’t get into in a podcast like this is known as statistics. Statistics is where you’re looking for particular patterns, probability of these patterns, it’s probability and statistics fundamentally, and improbable In statistics, you can have a single input, or multiple inputs, and multiple outputs.
In this case, what we’re looking at was, you know, the sleep pattern. And we’re looking at, actually the six patterns, right, you have six inputs potentially. And you’re looking for the output of two potential outputs, which means the baby stopped breathing, or it’s continuing to breathe.
So every time step, every minute, you’re getting six inputs into your system, the baby’s sleep patterns in one of those states, and potentially, is it breathing? Or is it not breathing? Okay, so trying to do a correlation of these sleep six states into these two output states got it. So remember, we talked about input and output. So that’s what you’re trying to correlate.
And when you’re trying to build an algorithm can predict one of those two states, sleeping or not sleeping from one of the six states of sleep? So, so this is fun, by the way, what I’ve just taught you is AI, the mathematics of AI. It’s an input pattern coming in, and an output pattern and can you can you predict that output based on it and a particular input? goes? Got it. So using a special program, or so we would build algorithms to do that.
So the algorithm varies by the field that you’re in. Okay. So at the heart of this is statistics that is currently being used.
So one way, by the way, you could model this is you take a bunch of inputs, you look at the output, and then you build a pattern, right of prediction. After a while, if the inputs and the outputs so good, so you’re so good at doing this, you can and the math is no longer based on probability. Now listen carefully, are statistics.
It’s based on an equation. It’s called a law. It’s not even so for example, let’s look at everyone’s probably heard of equals MC squared.
Einstein’s equation energy equals mass times the speed of light squared, or F equals MA Newton’s, one of Newton’s laws. force is equal to mass times acceleration. Well, how did Newton come across that? Well, he literally, that’s called the law.
It’s not statistics. It is that he literally had a table of I put in x force, right? I saw y acceleration. Right? And mass was a coefficient.
Okay? So like this, he built a table. And then he saw this pattern, hey, every time I increase force, acceleration goes up. But it’s a function of the mass, right? So if I have a body with, you know, mass x, right, you know, keeping the mass constant, and then he changed it.
So that was something. So F equals MA is literally a law. Okay.
So the question is, if you have a snake coming at you, should you run? Well, that’s a little more complicated. Can you build a law around that? Potentially, if you see enough snakes, right? And you see poisonous, non poisonous, let’s say those are the two outputs. So what neural nets were are actually a way everyone uses the word artificial neural net, you’ll hear all these, these nerds will use all these complicated language, but ultimately, statistics, okay, they’re just doing probability statistics.
The problem with the nerds is they try to make themselves more important than they really are. All they’re doing is statistics. And in fact, the field of AI is been frankly, fraudulent field in the sense because it’s been filled with people who basically been doing statistics for a living.
And they’ve been branding it as different things because they get tenure and they get professorships, etc. That’s a very simple way of putting it. It’s statistics.
Okay. Now, the So, and it’s a way of doing pattern analysis. So think about it broadly, these people are doing pattern analysis, think about the snake analogy, Snake is coming, when should I run or not? Okay, so what is pattern analysis? So pattern analysis, you can break it down into three fundamental boxes.
So if you have a piece of paper to if you have a piece of paper, right now you can put the piece of paper in landscape mode. And in the in the far left, draw one box in the middle, draw another box in the far right, draw another box, okay? And what you have is you have three things that you need to do to be able to do pattern analysis. And I’m basically teaching you what, you probably would not have the opportunity to learn if you took a course at any of these big universities, they make it so complicated that you would probably leave because they made it so complicated, but part of my doing these podcasts is make stuff simple, because I believe academics have a racket out of making things complicated, but those three boxes so the first box on the left, you can put in the word feature extraction.
I’ll tell you what, that is feature extraction. In the middle box, you want to put in the word clustering. And the box on the right, you want to put learning.
Okay? Feature Extraction for our left box, clustering, and learning. So let’s go back to the snake coming at you. Okay, what is feature extraction? Feature Extraction is called The Art of pattern recognition.
It’s the art of clustering is, once you get features, how do you cluster them into different groups? And learning is how do you learn from those clusters? Now, what I just said probably doesn’t make sense, but it will shortly. So I’ll give you an example. Just so you can see it says many, many years ago, for example, the CIA and the NSA and the government, and you should listen carefully, has lots of tools out there, we have cameras everywhere, they can take someone’s picture.
And they have it saved, and they do facial recognition. Okay. So if you look at someone’s picture, how do you know that this person or that person, okay, how do you determine that this is quote unquote, AI or pattern analysis? Well, one way you could do it, is you take a photograph, so think about you take a photograph with your high definition, iPhone.
Well think about that, when you take that picture, it’s 1000 pixels by 1000 pixels, okay? In full color, which could be you know, 64 bits of information, a lot of information is in one picture. Well, each little. So if you think about 64, I’m sorry, 1000 by 1000.
So what is that 1 million little cells, and each little pixel has color information in it, right? So you could build a database, a huge database of everyone’s image, which would cost you a lot of information to store. And then then you could start applying image analysis filters to do that, right? Well, that’s the way people are doing imaging. But there’s a simpler way to do this problem if you follow these things that feature extraction, clustering, and, and learning.
So let’s say you wanted to take a picture of someone and your pattern analysis are your AI job, Marcelo, and, Richard, let’s say, you guys were hired as AI engineers, build me a system that can take someone’s picture and tell me, are they Chinese? Are they African American? Are they you know, Caucasian? Are they whatever race right? How would you do that? Well, one is you can take the images and do some really hardcore analysis. But if you take the feature, so first step is feature extraction. So it turns out, you don’t have to save 1000 by 1000 a million pieces of data, it turns out that the art of pattern recognition is extracting a few set of numbers that you could reuse in the clustering process.
So what they found was you could take the distance between the eyes. How many numbers is that just one number. You could take the distance between the lips, another number, the distance between the tip of the nose to the bottom of the chin, another number, the distance between the years like this, you could basically you just need 10 numbers, okay? These are called features.
They’re not having to store every little pixel, every image, you’re getting the picture. And then you’re doing what’s called Feature detection. You write algorithms that can find where the ears are, and you say, okay, the distance between the two years is five inches.
Save that for Marcella, the distance between Marcel’s eyes is three inches. Okay, so those 10 features are enough to determine your race, what First you say those 10 features, right? So now you have a big database of people’s features. All right, then you start doing clustering.
Meaning you start saying, Oh, the Caucasian people all have this distance measure. So you basically have a little think about an Excel spreadsheet, right? Imagine for every person you have 10 numbers, you have an Excel spreadsheet. So if you say oh, when they have this distance statistically, between their ears and this distance between their eyes, etc.
They look like they’re Caucasian. When they have this distances, they look like they are Chinese because you have you can store this up, right and a huge database. So it is a you are humans making those? Yeah, so you would initially create what’s called a training set.
Okay. And so that training set will be used to cluster. So think about a cluster is like imagine you’re looking up in the sky and you see a couple of big clouds, right those clouds are clusters.
So one big cloud is the Caucasian cluster. Another cloud is the cluster of Asian Americans, right? Or Asians and other cloud. I don’t know, if I’m being politically correct here, doesn’t, it doesn’t matter.
But whenever you have these clusters, and in those clusters, you have the data points of people who are fitting certain measures you already know, a Chinese person, and then you look, so that’s a cluster. So you’re just putting people into those clusters, because you know, they’re Chinese. And then you do a learning, you say, Well, isn’t this interesting? The people are in the Chinese cluster all have this common distance.
Okay? Are the people in a Caucasian cluster? So now you’re learning? That right, so first, you identify features, you already know, this person is Chinese. So you’re putting them into these clusters. And then you write algorithms which measure these distance measures.
It’s called similarity distances, which is basically statistics. That’s interesting that you say the art of the would you say the art of AI, or the art of pattern recognition is interesting, because you would think it is feature extraction art, because it’s all it is. It’s called Feature Extraction.
Because one guy may figure out these 10 numbers, another guy may say, I’m going to store all 1000 pixels. Yeah, right? Another guy may say, I’m gonna store 50 numbers. Yeah, right.
So the art is, what’s a minute, because the minimum set of features you get is going to make your computing faster. Right? So obviously, law enforcement, they probably want feature extraction done really fast. So if you’re doing you need more computing power, etc.
So feature extraction is the art. What are the right features? Clustering is your you have some ability to because you have some a priori knowledge, which means knowledge that exists. And then the learning as you’re saying, Oh, look at those distances between for those features, they all seem to have this pattern you follow? So in this case, the input to your system.
So eventually, how would you create an AI system, you take a picture, right, run it through your feature extraction module, which is the first module on the left, which takes a picture calculates these distances, 10 numbers, feeds those 10 numbers to your clustering process, which stores them and then your learning process has Oh, the it does the analysis. And then by the way, once in a while, you may find something new. Let’s say you finally had a Native American, which you’d never coded for, that would say, Hey, this is a whole new cluster.
And then you your learning would say, Well, what’s new about this negative American cluster? And then you would learn and you would put that back in your algorithms. There’s there’s feedback. This is basically taught you AI.
That’s it. I guess the bigger picture is what is that all getting useful? Right? Some of that might be meaningful and useful. But then some of the other stuff, it’s like, what do we really need this data? Do we really need the time? Yeah, so it’s a good question.
So um, so once you start learning, you acquire lots and lots of data. And then as your system gets better and better and better, by the way, this is called Confidence level. So when you’re doing these, so when these AI systems are being trained, initially, your accuracy is probably like nothing, right? And then your accuracy increases, and it gets better and better and better.
Eventually, your AI system. This system is doing as good as a human being or even better, because it can notice subtleties like you can have Chinese people who grew up in America versus Chinese in China, like you can start splitting these clusters and even finer clusters. But so I’ll give you a couple of examples that I’ve worked on that shows you that the same process you apply so I gave you the example of sudden infant death syndrome, while same thing.
What we noticed was the features there were the states of sleep. Right? Those are the six features, the different states of sleep over time. Many years after I did that work, when I first came to MIT, there’s a very interesting phenomenon called to DOMA.
Tado ma probably have never heard of that word. But it’s how deaf blind people communicate. Think about if for a second if you close your eyes, and you are deaf, and you cannot see how do you communicate with another human being? Helen Keller, if you remember, was deaf blind.
So deaf blind people, if you go to see a deaf blind person or you want to communicate, if they learn this technique called to DOMA, they’re able to actually understand you. And what so there was a speech processing group at MIT 1983. And MIT is to have the same call Europe’s undergraduate research opportunities, programming think I mentioned to you in podcast one, how I work with Chomsky to understand the caste system when I first came in, but one of the other Europe’s I did was to understand up to DOMA work again.
I was very interested in understanding patterns. analysis. So here, what happens is into DOMA, the deafblind person takes their hand, and they put it on your face.
And interestingly enough, they put it on your face. So they’re actually getting different signals. And the purpose of our research project, there was a guy called that door lock, which was the faculty sponsor, and I was working with a graduate graduate student called Greg Scarda, who was doing this for his master’s thesis.
And I was just helping these guys out, doing the signal processing. So the idea was to find out what are these people listening to? Because they’re actually able to understand you or the other person talking? The other person is talking. Okay, so they’re feeling the the movement of the hips? Maybe that’s one what do you think, Richard? Yeah.
So what are they listening to? Well, let’s just step back, it turns out that one square inch of your finger pads have more sensors in your retina. So think about that. You ever hear this thing? You can, you can look but don’t touch? Yeah, there’s something to that, because your fingers are amazingly sensitive.
So what So what we found out was they were actually listening to seven different signals. So when they put their hand on your face, they’re listening to the your upper lip moves up and down, your lower lip moves up and down, but it also moves horizontally. So there’s three signals there, they’re actually able to sense where your tongue is the position of your tongue.
On the palate, they’re able to also understand your breathing, and your jaw movements. So it adds up to around seven signals. So by purely putting their hand on your mouth, you’re able to capture seven signals.
So what we did in the lab was we literally created a device, where first of all, we actually brought in people had them speak, cat, dog, mouse, and actually had their waveforms. And they were actually measuring the seven signals. So we were creating a database.
And we were using these features, the seven features to map to cluster to these words, you follow me. So here, the cluster were words, and we have the seven signals. And then we were doing learning to figure out how the seven signals matched to actual waveforms of individual words.
So as you did more and more and more of this training, we literally had one of the guys in the lab that actually built a retainer, it was a very interesting retainer you put into people’s mouth, and the top of the retainer could measure the tongue position. So we knew where every tongue position was, we knew the waveform of their breathing, the upper lip movement, the lower lip movement, all the sudden signals, just what that person was doing, then we were matching those seven signals to the words. So here the signals were seven waveform signals being matched to, I don’t know, 20,000 words in the English dictionary.
Okay, so they didn’t have to do all of that to learn that. So how did they learn what they were doing? So the human brain? So the thing is, exact? Great question. So the human brain is doing this learning.
And we were doing this learning on the computer. Okay, but how did they learn it in the first place? Well, so how was the brain doing that? So there’s different theories about learning. One of the modern theories is that, that the brain actually has computing centers, like it actually has computing.
And it actually may be framed around an artificial neural network, that you give it an input. And you give it an output, give it an input, and give it an output. So for example, when you’re learning to ride a bike, you’re learning and you say, Oh, when I distribute my weight, that way, I keep going forward, oops, when I do that, I fall.
So the brain is getting inputs and outputs, inputs and outputs, inputs and outputs, just like those chess masters, you give them over and over and over again, they learn and the brain learns to generalize. Okay, when it sees some other pattern that normally didn’t occur, it’s actually able to even learn from that. So the one of the theories is that it’s through things like what if, by the way, this is called multi level nonlinear regression, basically a big falutin term to mean your brain is doing probabilistic calculations.
And it’s learning and learning and learning. So it turns out that again, repetition is a mother of skill. I think I mentioned to you that I think there’s some guy wrote a book, I don’t know who it was.
If you want to learn something, do it 10,000 times. Right. So if you want to learn to be a great tango dancer, do it 10,000 times.
Now, some people can do it 5000 times, some people 7000 But there’s a certain number of repetitions you have to do, that you can learn. And that’s by the way when you train neural networks. That’s what you’re learning to do.
So for example, in 1993, I wrote a paper with a number of other colleagues It’s an MIT on handwriting recognition. So if you, I don’t know how many of you write a bank check anymore paper check. But if I were to write you a check for $100, I put Marcelo Guadiana.
And you know, in the little box, I put 100 point 00. And I put and I have to write $100 X X slash, right. So the national West Bank in western bank in London had funded a project that I headed up to create a neural network system to see if you could take a bank cheque scan it, and could I predict the exact number like 100 bucks, because everyone writes different handwriting.
So how do we do this? Well, first of all, we had to do the feature extraction, right, which was to be able to read that box on the check and extract out the number of digits, that was a feature extraction. And then we have to do feature extraction for each digit. Because people, right some people just write a one with a straight line other people put a little hook on it and write it.
Right, zero. So how do we get all the possibilities? We actually went got census data. Turns out the United States Government saves when you fill out a census, all your handwriting, so in a big CD disc, of all the ways people write the number one all the way people write the number zero, because you just needed to learn that 10 digits, right.
So when whenever you drew that digit, we learned the stroke sequence. So we didn’t get to have to change all the learn all the pixels. And we save those stroke sequences for each digit, clustered them, and we train the system.
So it could, it got very good, right 98 99% accuracy, that we could figure out a zero or one etc. Anyway, so I built this whole system. And I remember this was in the 90s, I had to go do a demo, I was literally to Heathrow Airport from Boston carrying a huge scanner on my backpack with the computer and we went demonstrated it well.
This is a 1993. And that was and that paper was written in the International Journal of pattern analysis and recognition, you read it. So So again, we’re using the same methodology for recognizing handwriting, same methodology that’s being used for analyzing to DOMA.
So you know, for me, this was a journey starting in 78, which SIDS in the 80s, with Tacoma, in the 90s, with handwriting recognition. So 1993 1994, I was very much into pattern analysis, and I came to the conclusion that the whole field of AI was a sham, because everyone was doing the same things, which was feature extraction, clustering, and learning, they were all putting different terms on it, to own their academic discipline. So when you did speech recognition, they call that speech recognition.
Other people call it, you know, in their field, and they’re trying to, and other people would call it sleep analysis. But if you looked at the fundamentals of this, it was feature extraction, clustering and learning that statistics, its statistics. So I said, could I build a common system, that I could use a new technology, a platform that you or Richard can use to recognize, to use it apply broadly to all different types of universal problems, whether the signal coming in was asleep pattern of the baby with his when the signal was a handwriting thing, or the signal was a document or speech? So that was the basis of my thesis work, which I called information cybernetics.
So when I came to him, and so in 9394, after I finished my master’s i, and by the way, my thesis never fit in any one department. It wasn’t a mechanical engineering thesis, it wasn’t a electrical engineering thesis. It was an immediate thesis, right? Because I had degrees and all those three, so I had tried to create my own department.
It was an interdepartmental program, it had to get approved by the Dean of the School of Engineering with the fight for and so I was doing that work starting in 93. And I was making headway into creating a whole new foundational base for doing any type of pattern analysis problem. And while I was doing this, I was called to participate in a competition for doing analyzing President Clinton’s email.
This too, was a pattern analysis promise. So if you remember 93, not Hillary Clinton, right? No, Bill Clinton is now this is Bill Clinton and email everyone thinks Hillary, right, right. This has to do so well.
Remember what I said the invention of email. The system as we know today took place in 1978. An email was used in the office environment.
In fact, in 93, when I used to do seminars, a roomful of 1000 people yes how many people an email account very few people at an email can maybe two people. But if you went to an office environment like lotus or IBM, people had email accounts and by the Leone the internet for email, people were using email by interconnected computers that I’m right. But in 93, if you remember something interesting occurred, the World Wide Web came www, which was a front end graphical user interface to the internet, which made now the internet accessible to everyday people point and click.
Well, when that occurred, people started creating web based versions of the email system that I invented, Hotmail, Yahoo, all these came out. So what did that mean? Now millions of people started getting email accounts. And millions of emails start getting sent.
So the White House before when people wanted to write to the president, they would write a letter. Dear Mr. President, I don’t like your program on education.
Dear, Mr. President, I don’t like your program on the Middle East, right, or I support your position on education, right? Well, when these letters came into the White House, physical letters, the White House, believe it or not, had 147 buckets. And these buckets returned education, bucket, drug bucket, you know, threats.
And for each one of those buckets, they had a form letter, or sometimes they would get routed, and they literally had a form letter. So if you sent the president email, you know, I support your policy and education, he would say thank you very much, Marcelo. And here’s my you know, they’d have, here’s my real policy, and they’d have a staffer sign it, and they’d send it out, it was called the correspondent management system.
So print mail set in the bucket it into one of those 147 buckets, and they’d send you out of print mail. Well, when email came, the White House was being inundated. Okay, and they were using interns to do that.
And again, I said, probably shouldn’t use word interns with Clinton. But that’s what they were doing that. So as email volume starts growing.
Now think about what they would have to do that time, lots of interns, so now they’re getting way more mail where they’re gonna get because email is so much easier to send, you don’t have to put a postage on it. So the emails are coming in and guess what the Clinton White House is doing? They are treating email like print mail. So when an email came in, they would print it out.
And then they would only answer back to those emails, which had a printed address. So they print out the email, they would look into one of those 147 different buckets, and they would send you back out a print letter to your email. Seriously, they’re not think it would be easier to email them back? No, you have to understand people are they weren’t ready for email such a new thing.
That’s what people don’t understand this was new. So the White House runs a competition, an AI competition through the National Institute of Standards, which used to run this conference called Text retrieval conference, which was like an AI conference. And six publicly traded companies were invited, I was the only graduate student, which was because people knew I was doing something cool.
So I was invited to participate. And this is what the government did in this contest. It was a blind contest.
They gave you a bunch of emails. And they gave you the 147 buckets. So the emails are what your input, the 147 buckets, your output, so you had to take those and there were too many that you couldn’t do it by hand.
Okay. So you had to your system was measured on its ability to take an email, and would have put it into the right bucket, they already had scored the right buckets. So you’re looking at certain words, probably, well, the input is a document that is an email, and you have to put into the right buckets.
Okay? While the long long net of the story is I use this technology I created, which was a multi hybrid solution, I didn’t just use one technique, I threw everything at it, because I realized that all these academics were doing little simple solutions. And then they were blowing it up. So I you through.
So in my feature extraction, I had like seven different feature extraction methods. I had like 10, different clustering methods, and three different learning algorithms. You see, I took an engineering approach.
I wasn’t wedded to any one approach. And anyway, long story short, I ended up winning this competition. So you had to find a way to separate the emails into each one of the buckets.
So if you got an email they already had scored. This email was about education. This email was about this right.
Some of them were death threats, and they had multiple different types of threats. So how did you figure it out? Well, so I had built a set of ways to do the feature extraction, right. And what I came to the conclusion was that every email had an attitude.
Okay. Every email had different things people wanted. Every email had a different type of object of concern.
Every email had different issues, and every email always told what people want it. So I found these five features five to seven other features of art and pattern recognition. So I did that art because I just had the aha moment.
I said, You know what, when people write an email, they think they’re writing to someone they say, Hi, my name is Bill. I’m a CEO of a company. President Clinton.
I really like your position on education. I’d like to know what you’re going to do for small businesses. Can my son get a tour of the White House? Right? And can I come and speak people? So there were multiple issues in there.
So I figured out that thing and I started building clustering algorithms based on keyword frequency. And then I learned from that, okay, so I built this very powerful system I ended up winning. And I in fact, one one of the MIT still has this award called the Lemelson MIT awards.
MIT Lemelson awards. And I was a finalist that year with four other people, it was pretty big honor to be a finalist, I think the year that I won another guy built a flying car. Okay, so but I was a finalist.
And after I did this, my lawyer said, Shiva, you can always do your PhD. And the internet is exploding. And he said, Why didn’t you leave and start a company for a couple years? So I left MIT.
I went to MIT. And I said, Look, I built this. I did it on MIT time, you have to give MIT the rights.
Do you want? Can you give me a waiver? So I said, MIT said, we don’t think it’s gonna go anywhere. You have to give them the rights because you’re working on it because I was a graduate student at MIT. But MIT has a provision you go to them and their technology licensing the way MIT’s intellectual property structure works is if you develop anything, and you’re being funded, I think I may have gotten some grant, I don’t think so I don’t think I I was even being funded by I was paying my own way through it.
So but I still was, in following the ethics, I went to Lita Nelson, I said, lead, I think I’d build something, she goes, I don’t think this email management stuff is gonna be important. Because wherever this is, 93, they didn’t know how big email is going to be. So they gave me the waiver.
And I left MIT and I started a company called Echo mail, like the waves, you know, because one of the areas of pattern recognition I was also doing was a military project, I was working in a lab that was interested in by the when you build Wings of airplanes, they’re made with composite materials. And sometimes deep inside that material, some of the fibers can be breaking. So we used to send ultrasonic waves into the thing, and based on the wave that came back, could you predict what was going on without having to open up a billion dollar wing or $100 million wing, right.
So that’s why if you look at Echo mail, the symbols were these three waves, because we’re sending, so echo mail was the company I started, just jumped out of MIT, we had no customers nothing. And my first customer, as I mentioned before, I went 40 times back and forth to AT and T convinced them to use eco mail, you know, and all I had was a core technology and built a software yet. And so they started routing all their email to us.
And our technology would analyze the email bucket. And then we had humans review it. And if they said an email was correct, we learned from that.
So our system got smarter and smarter and smarter. And we would get $1.83.
For every email, we categorized right 30 cents for every email that went through the system. And I grew that to run a 250 million value company. And we sold a big piece of that.
And in fact, now we’re going to relaunch that company for small businesses. It’s called the AI in email. But the point is that the same approach I took to analyzing emails, the same approach I looked at for DOMA was the same approach for sudden infant death syndrome.
But the foundations of AI, or pattern analysis are feature extraction, clustering and learning. So you basically gotten a course in AI, you know, so when someone tries to highfalutin you, that’s all it is. And either you should ask someone, oh, you’re working on AI? What are you working on? Are you working on clustering methods? You’re working on feature extraction? Are you working on learning? And they’ll they’ll, they’ll know, you’ve sort of understood what what they’re doing, you know, and but that’s what people are doing.
They’ll throw out big terms. I’m working on SVM. I’m using neural nets, I’m using, you know, higher order clustering, but they’re using they’re doing one of those three things.
They just try to make it more compact. Yeah. Right.
But that’s what they’re doing. Okay. So the field is fundamentally that.
So I think you fundamentally, I think, understood the foundations of AI. From a technical perspective, I think there should be another part added to that, that we’re not thinking about is what’s the bigger picture to the AI? Like, what is creating for humanity? Is that something we should pursue? Or not? Like? Is it bad for society? Right, it shouldn’t there. Yeah, that’s Yeah, so I think we’ve, so now we’ve talked about the guts of AI.
Now, I think, again, you guys should jump in on this part. Because I think now let’s move to the, what does this mean for you? And what does it mean for the future of you? So what we’re seeing is, as computing has gotten better, and better and better, because remember, feature extraction requires computing. Clustering requires computing, learning requires computing.
So as the technology gets better, we’re able to transfer More quote unquote, Pattern Analysis methodology with which a human being has to the computer, across many fields we talked about before we started radiology in medicine, a radiologist who goes through umpteen years of undergraduate umpteen years of medical training and other 10 years or whatever of clinical training practice, he learns how to look at a x ray, and learns to predict, okay, does that person have a brain tumor or not? Right? Well, if that radiologists can be used to train a machine through feature extraction, clustering and learning, you’ve transferred knowledge, okay? You take something like designing something, as 3d printing comes right? As these new technologies come? Can you actually take soft skills, how people do design, another AI project I worked on at the Media Lab was if you look at a cereal box, or you look at a poster, someone’s doing that design do you call a graphic designer, so I came up with the method that the system would predict all different favorable designs, it would come back, so you didn’t have to go through design to say, here are the best designs. And you could check one off, right. So the reality is, we’re going to be able to take a lot of human skills.
And through this process of feature extraction, clustering, and learning transferred to, from what I call the carbon based machine, which is you to a silicon based machine, which is something outside of you. So just think about that broadly. And think about it that you can in fact transfer potentially, depending on your viewpoint of consciousness, from your brain to a silicon based brain, nearly every major country is running what’s called a brain project.
So what they’re doing is trying to simulate, they’re saying if we get enough nor neurons, artificial neurons packed into a computer and create all the interconnections, could we not create consciousness? So, the theory in brain sciences goes like this, and this is an age old theory is it? I think, therefore, I am, which means are I am therefore I think, meaning does existence precede essence or does essence precede existence, this is a whole mind body problem. So the spiritualists or the idealists argue none of their something separate from us than the physical body, that gives us consciousness. The materialists argue, known that human consciousness is a material artifact.
So if you take a slug, you know one of those slugs, it only has about five neurons. Human beings have millions of neurons, and we have millions of interconnections. So the argument is that you could create a slug on a computer, or you could create a human being on a computer if you connect enough neurons and you train it.
Okay, so every major, so the theory is, if you could do that, one could argue that if you’re about to die, and this is sort of a wild concept, you could transfer your state of consciousness, everything you are to a silicon paste machine, that if you’re a materialist, and if you think that the con yes is part of your brain, so this is going to be the so so the that’s not necessarily the case, right? That’s, well, we don’t know, we don’t know I’m I’m so here’s, in 1994, when I was working on eco Mal, I had I used to do in, I still do a lot of meditation, and it’s, you know, I’m very interested in these other states of consciousness. So I got up in the middle, I was in this dream. And in this dream, I was sitting at a table.
And at that table across from me, was a, what looked like a robot. And it looked like a human being, but I knew it was a robot. And the question that came to me to answer this bigger question is, what is the difference between me and that being across, sitting across from me? And this is the foundational question of pattern analysis or AI? Because, look, if you look at the arc of technology, let’s assume one day you could create something that looks just like you, in fact, can feel can cry, because there’s a whole field of computing now called effective computing a FF e c, t IV, which means computers, you can actually teach them emotions.
So if you have in fact, there’s a computer that as you look at it, if you smile, it smiles back at you, you know, so this is let’s assume that we’re going to get over all those technology hurdles, that there will be something that looks like you talk like you and can pretty much do everything. You do. And but it’s not made up of, you know, genes and proteins and blood and plasma, it’s made up of something else.
The question is, what is the difference between you in that? Yeah, that’s a question that came up in the stream. And the conclusion I came to, was that there is fundamentally no difference, like from a mechanical perspective, or from an electromechanical perspective, from an even a intelligent perspective. But the difference would be which of those creatures has asked the question about who am I? Why do I exist? And where do I come from? And that is what I believe makes us human.
So I would argue that, that you could have machines, which may be more human than we call humans today. But even if the machines are you that they wouldn’t they would steal the machines, right? Well, this is this is an interesting question. So what are we a machine like, you know, look, my degrees in my PhD in Biological Engineering, and at some point, we’ll do a podcast on when you actually look through a microscope.
And you look at what we’re discovering in biology, man, and you look at a piece of DNA, and how that DNA opens up, physically opens up, it sends out a piece of messenger RNA, to the ribosomes, and that ribosome, like a little punch thing, makes, you know, proteins. It’s a frickin factory. And you look at that, and you see, this is pretty amazing, that we actually are a machine, a molecular, you know, going down to the molecular level.
So I would now how did we get here, I mean, we can have arguments. But let’s take this argument from a natural law perspective, that nature is this amazing engineer, you can call nature God, you can assign it whatever spiritual or non spiritual value on but nature in a creative way, like an engineer has been testing and testing and testing. It’s been doing its own pattern analysis, throwing away stuff, keeping stuff.
And it’s created this thing we call the human being, which is made up of carbon, and phosphorous and the different elements. But if you go at it, man, we’re it’s a pretty amazing finely tuned machine. Yeah.
And this machine has the ability to extract features, it has the ability to cluster it has the ability to learn. So the argument is, if you one day had Marcelo or Richard, replicate it with everything you can do in another machine, what is entertain you in that machine? Is there a difference? And I would argue, based on going back to our second podcast, what is a system? Are you an intelligence system, are you an open system, and intelligence system takes an input and gives an output and intelligence system has a goal, it has a mission, and it is engaging in the world through some goal, to correct itself, make constant changes in the face of disturbances and a struggle to achieve that goal. So I argue that intelligence is having a goal, and working towards a journey of achieving that goal, through struggle through disturbances.
That’s a systems perspective, I would argue that anything is not human, and is artificial, that just takes an input and spits out an output. So think about a number of humans, quote, unquote, humans who think quote, unquote, think, who if they’re watching Fox News, or CNN, based on what they get, then they go pull the lever for Republican or Democrat, just think about that, they get an input, they go do an output, input output. And how many of those human beings are asking setting a goal for themselves, I want to be a free human being, I want truth, freedom and health.
That’s my goal. And I’m going to find that in the midst of all the noise and the disturbance, I’m going to take actions to find that for me, so they set a goal, I want truth, or I want freedom, or I want health. And I’m going to take the output of that what I’ve seen in the world, and I’m going to engage in that to interact with the world to get that for me.
And then another set of human beings get an input and an output. So I would argue that you it doesn’t matter what what, where that consciousness resides, it could be in a carbon based creatures silicon based creature. The issue is, what is that ability of that Hume that thing? To go down that path? And I would argue that we already have AI, which, to me AI is that ability to just take an input given output input given output.
What I’m so good at those, like, if we do create conscious creatures, how do we know if they’re actually con just right and right so there’s gonna be a sharp different leg a sharp difference between humans and robots that we think are conscious. Right? I think there’s a lot well give me an LED me. So in my definition, I would argue some humans are robots already.
Yeah. So So you look at a look, we had this thing, when we ran our campaign, as you know, call only the real Indian can defeat the fake Indian. Okay.
Was it was a it was what is it? It’s like an image on some plastic, which had a picture of me picture of Elizabeth Warren and a headdress. And it said only the real Indian can defeat the faking it. So I presented that to some quote unquote, humans.
Some humans, they saw that input would laugh. Other humans would say, hey, what do you mean by that? I want to understand that other humans would freak out and start attacking us calling us racist, calling us names. Okay.
Now, I would say the first and the third group are robots. The second group wanted to understand that is a human being. So you, you follow what I’m saying? Yeah, input output.
So the input comes in, they already have certain transport, conversion to storage in them, storage of memories. And it’s like a reaction. It’s no different than a slug.
When I poke it in one way, it goes this way, when I poke it in another way goes this way. Yeah. And so my argument is that that notion of an input coming in you process in an output, that’s not a human being would remember, we call that an open system.
A human being, or whatever you want to call human, like intelligence is something that has a goal in mind. And I would argue the level of consciousness is a refinement of that goal. Right? Yeah.
So you could call it enlightened, you know, from the concept of self actualization and enlightened human being, his goal is not just to get food or sex, or own a piece of land or, you know, make money. Yeah, there’s really In other words, they’re not that like conditioned or brainwashed by the system. Well, here’s an interesting thing is they actually questioning where their goals even come from.
So this gets into this gets into, where do the things that I want. So we talked about an open system input output, I would say, that’s a dumbest form of a human, or that’s a robot, you get the next one, which has some goal. And then you can get into the quality of these goals.
So you have people you know, when I was at, when I first came to MIT, there were the students would come to MIT. And I would argue, and I give you two things. I think if you and I spoke about this, I would literally see the so called intelligent humans act like robots.
In my freshman year, these young kids would come in 1718 year old kids who looked normal, they stood up straight, they talked with a certain tenor, or didn’t have any tics or weird movements, then they will go see some professor speak, who talk with a highly nasally voice had some Txy be flailing his arm in a little weird way, like something was wrong with him. But therefore, he must be intelligent, because he’s idiosyncratic and eccentric. And I swear to God, the students will start behaving like that professor.
Yeah, remember and speak with a nasally voice like this and flail their hands and use their same vocabulary, right? Yeah. You saw this. I’m telling you, you saw these people start emulating another creature to be accepted into this framework of now you were an intelligent nerd.
So what do you think it is that makes them the person? Like question their surroundings? And step out of the the box? Yeah, this is a good question. You know, because I’m thinking maybe it just like, I just like kind of alienating yourself a bit from assistant not society, but like the system at the TV. And read because that’s when you start thinking about yourself, right? You’re not like always Yeah, this is I think this is like a part of the AI.
It’s always just like meditation in a way where you have to be by yourself and really, truly think about where you came from, and why you’re here and what your goals and your purpose in life. I think to answer this question, I think at some point, someone has to have some crisis, some crisis where they are forced with the loss of something that they love, or they question their own death. Or they I think it’s out of loss is where you start asking these questions.
So for me, these questions came when I saw the loss of my own innocence where, you know, when I was five years, four years old, or I’m playing with this kid, and I go to the mother’s home, you know, this, my friend, I thought he was my friend, and the mother force Just need to stay outside and gives me water in a different glass and calls me should read, which is like calling me nigger. Yeah. Okay in India because we were considered that’s when I didn’t even know what’s going on.
That’s when I consider there was this thing called a caste system. So I was open to that as a forefoot. And then I learned my mom was chased away.
Like, wait a minute, what, what the hell is going on? Human beings? Their separation? Yeah, that hurt is what forced me to start questioning everything like what the hell is this? Here, I’m playing with this kid and I go to his mother’s mother mistreats me like this. I’m no longer a human being. You took the path.
But then there’s the other path was which a lot of people take which is almost like resentment and saying, oh, that’s the way things are. So that’s the way things are going to be. But you as a little boy were like, no things can change.
But I think it was my parents who had fought over that they didn’t believe in destiny. So I think this comes to the crux of the issues, John Paul Sartre. He wrote a book called being in nothingness.
And Sartre. He was one of the areas of existentialism, existentialism basically says, existence precedes essence. Which means your existence from your existence, like you’re born at a certain point in time.
I was born December 2 1963. When were you born? November 17 1994. Richard, when you were born January 21.
Right. So you were born at that point. So think about you’re born, you come into this thing called the world.
Now, you don’t know anything that occurred before that. Let’s take a non spiritual perspective, materialist perspective for a second, okay. And that may be an accurate approximation, we don’t know.
But let’s assume that, right. So at that point that you were born, you are now from the instant you’re born. One could argue that your entire life from that moment, you get all these constraints from society.
So if you’re born into a Catholic family, you’re supposed to behave like a Catholic, you’re supposed to do this and you’re supposed to, you’re born into a Hindu family supposed to be like this, if you’re born into an African American family, in a ghetto, you’d be you said, I’m saying, yeah, so Sartre talked about this. And he gives an example of a waiter, who he sees at a restaurant. And the waiter comes to serve Him, and He’s dressed a certain behaves a certain way.
And he behaves like He cocked his head at all these mannerisms. And he notices all the waiters who that like they have to behave like that to be a waiter. So what happens is the fundamental dragon here, which we all have to slay, is that the universe or the world is trying from the instant you’re born to control your existence, to minimize the infinite possibilities of what you can be if to a finite set of possibilities.
And the struggle of life is to recognize that the aspect of your existence is actually being an infinite human being, there’s you, are you not your past, yeah, you can be anything. And if you truly look at marks, which a lot of people don’t look at on the left or the right, they don’t really understand this. Marx was addressing the human condition.
In the first chapters of Das Kapital, he talks about the notion that a finite set of people, a very small set of people get to live their dreams. And the rest of us have to be controlled like automatons. So he’s already talking about artificial intelligence.
So we need to move away from the single art of no artificial intelligence been here for a long time. And so the issue really here is that what Sartre was saying, was that a, you don’t owe anything to your past, that what matters is the choices that you make, the goals that you set for yourself, the dreams that you have. And one of the fundamental things is that monopoly capitalism, which means the drive to purely make profit all day long, just is essential goal is to destroy the human being, because it is about maximization of profit.
So why are we creating robots? Why did we create the manufacturing line? Let’s go to the depth of it, the economic analysis of it, because Henry Ford wanted to make a lot of cars for a low cost, so you can maximize profit. So remember, basic accounting 101. Income, net income is equal to revenue minus expense, revenue minus expense.
In any industry, the number one expense is labor. So you’re trying to take you sell a pen this pen I’m having for 10 bucks, it costs you five bucks to make it how much profit do I make? Okay, so how can I reduce profit? Well, I get the materials made cheaper materials can Be one line and expense, or I make it cheaper. Well, I go exploit someone, and maybe the material to dig up in America to make this pen cost me two bucks.
But I can go over to El Salvador or Haiti and get it done for 10 cents. So I’ll do that. And then I find out, okay, everyone’s going to El Salvador pretenses.
Well, now I’ll apply machine automation that can make it for a penny. So what we’re trying to do, or the capitalist model, is you’re trying to lower the cost of goods, and you’re trying to lower the cost of production. Yeah.
So either. So the goal, ultimately, if you’re dealing with a human being, you want to treat them as a commodity. So you want to peel away any humanity that they have.
So you do an assembly line, right? Yes, he just become what he was like it was being a cog in the machine. And you love capital and capital, and you lose the individualism because everything’s done for the corporation. So it’s more for the maximization of profit, yeah, to be very mathematical about you want to maximize profit.
But like it just I’m thinking of any huge corporation Nowadays, everyone gets so caught up in doing everything for the companies, for the company, for the company, you have to make it go. It’s like it takes a life form of itself. It’s an organism itself, and becomes most important thing in life, right? And you lose all that sense of individualism.
Right? And well, well, the ultimate the ultimate form of this is what’s going on right now. That forget even labor there are people just move money around all day. Yeah, they don’t do it.
They’re so far removed. They’re moving capital around, and they move. By the way, $600 trillion is our economy in the world.
So think about every instance $600 trillion dollars is being moved 600 trillion. It’s unfathomable what that is, right. So as you know, we think the United States, by the way, 20 trillion, that’s our GDP.
But 600 trillion, is the amount of money in the economy. So that money’s being moved, there are people who move money from point A to point B, and make billions just from that movement of capital, they’re not doing anything productive, what you would call productive work, labor. So that movement of capital, and there’s machines now which are getting ready to do that, okay, high frequency trading machines.
So now you have algorithms competing with other algorithms. So the when you really look at it, the dehumanization that monopoly capitalism affords is at that level. Yeah.
So talking about what makes us human. And relating that to AI? Do you think we’ve gotten more conscious because of, like the internet and like, all this information that we’re constantly exposed to keep? Or do you think we’ve kind of like dumbed down? And because everyone’s like, so caught up in their phones? Do you think we’re more like, yeah, outputs to then what let’s, let’s think about it this way, right? If you go back to what I just said, about what is the focus of monopoly capitalism, and let’s talk about the notion of this machinery that’s being created. Okay.
So, in my book, The Future of email, I try to teach people that there’s two principles control and observability, controllability and observability. Just sort of just think about those two words. So the machine wants to be able to observe you at all times, and be able to control your actions for because it has its goal.
Okay, so the few who don’t want to get into conspiracy theory, but just think about the maximization of profit, right, so think about Edward Bernays, the guy who’s known as a modern modern guy of advertising, okay, the entire goal of advertising is to sell you something as fast as possible. And so they’re so what they want to do is to identify groups of people who you can sell stuff to, and then control their behavior. So they’ll eventually through a series of interactions, buy something.
Okay. So in order to do that you need to observe, get lots of data. And then at certain point, you need to control their behavior.
So they’ll eventually go to in the case of Edward Bernays, you know, the guy who did started doing some the early cigarette advertising drive you to go and buy buy a cigarette. So some of their earliest ads, which they understood, wow, the women’s movement is coming. Let’s have women look strong, you know, and smoking cigarettes.
Okay. We will take advantage of this movement of the growing independence of women because they observe that and move them towards buying a cigarette show A smoking a cigarette makes you independent. So the ability to do associated memory like that juxtapose two things to go to a particular goal.
So observability and controllability. So those in power want to be able to as many, many sensors, as we talked about, get lots of data. So how did they get data before the internet came? Well, they did surveys, they call up people, right? They get survey data put into a database and say, Oh, these people over in Massachusetts like lobster, okay, they’re trying to put lobster ads, right? Oh, these people down in Florida sunny environment.
They like suntan lotion. It’s pretty easy, right? We’ll market suntan lotion to them. Well, now it’s more sophisticated.
Now on the internet, with social media, they can watch every click you do everything you’re viewing where you’re scrolling, etc. So now every one of us in some database, there is an email address to view some identifier, and they’re collecting oodles of information about your behavior. Right.
And from that input, they’re extracting features. Feature Extraction, they’re doing clustering. Oh, Marcelo Guadiana.
Student, right? Likes journalism, libertarian. Richard Giorgio, Cambridge resident, you know, independent thinking, right? They’re, they’re, they’re clustering you. And they’re watch your behavior.
So now they have you categorized which is clusters. And the next step is controllability. Okay, when Richard is doing this activity, for example, on this website, at this point, I’m going to hit him with a particular message.
And I know if I hit Richard, like at&t knows, I have to hit people 17 times at the message and they get an output, I’m gonna hit Richard 10 times five times they know, it’s called a conversion rate. So you are basically being modeled? You your dynamics are being known. You are basically, in the larger world part of AI.
Yeah. Okay, part of a larger pattern analysis system. So how do you break from that? Well, are you a particular cluster? Okay? Are you going to let yourself be observed? Are you going to let yourself be controlled? Because ultimately, the goal here is to think of you in one of those clusters, and then manipulate that cluster.
So the idea really comes down to what what about, you’re not in any one cluster? What about you’re in in many clusters? What about your more chaotic than they understood, hey, I may be an engineer, but I also may be a politician. And I may be a writer, I may be an artist, I may be infinite. That goes back to what we were talking about earlier about breaking these boxes up, people try to put you in, right and the time you’re born.
And now it seems like that’s the plan. That’s the idea. What you just said to put you into a cluster, it’s that middle box.
Now they have to do it to maximize profit. Exactly. It’s like now it’s gotten way worse, right answer my question, because it’s gotten way worse.
And what we thought the internet was, was a liberating medium, could actually be a vehicle now to even centralized power even faster. Yeah, you see, whenever new technology comes, everyone thinks, oh, the printing press came, I can all print Bibles, we can all print our own documents, right? Like we can all be publishers. Well, what ended up happening is you created five major publishing houses, it’s hard to get your book out on the New York Times bestseller unless you’re on the end.
Okay, when the internet came division was, oh, we’ll all be able to get our knowledge out. Well, now you have Facebook and Google and Amazon. Okay.
In fact, you could argue that you can be wiped out faster now than ever before, which means I remove your name from a Google index, Marcella Guadiana, does not exist. So what we’ve done is, we’ve seen the consolidation of power. And the only way to overcome this is to actually become a true human.
Yeah, and this notion of becoming a true human is a deeply deeply important one, because it means you have to ask who am I? Am I an American, right? Am I a Cambridge resident? Right? Am I this are that people have to recognize that they have infinite possibilities of what they can become. And that they have to say I don’t owe jack shit to what I was born in and what I owe in the past. It’s because every to your point, every decade that goes by the manipulation is going on to when Richard has a child or you have a child, they already know your past.
Yeah, they know the trajectory of your children. Right I was just gonna get it. It’s called Cradle to Grave marketing impossible to break in a way because we’re, we’re raising generations like this so the kids are gonna see their dad like that.
And that just gonna be the way that’s why The only way out of this is what I call you have to have it. The new food the new mana is not land bread or peace. It’s the mana of truth, freedom and health, which is going to come from a systems thinking.
People need to recognize that they need to start having a new framework for thinking. They need to recognize that everything that they are, comes from observability and controllability and that they need to recognize that they don’t owe anything to their past, meaning the past. I’m not saying you don’t owe anything to your parents or those kinds of things, right.
But I’m taking it from a thinking perspective. Otherwise, you are a robot. Give me another example.
When I first came to MIT, you know, I’m an Indian American born here, but they’re the Indians who came from India. The graduate students. These guys were robots.
They came here from India. I have come to MIT. I’m so happy to get in.
I’m the best in the world. I’m going to get my graduate degree then I will go back to India get an arranged marriage get married, then I will come here. Live here.
Get my BMW. I’m a success that they’re on a freakin program. Right? Total freakin robots.
Yeah. Would you say intelligent? I’ve called them just basically educated idiots. So what we’re creating that is a stream of educated idiots.
And even more disconcerting, these people actually think they’re intelligent. Yeah. Well, that’s what I call that artificial intelligence.
That’s how a lot of people define intelligence nowadays, right? Like, if you know math, science, like how much money you make. But the other huge question is, though, the humans becoming cyborgs. So, Elon Musk has talked about this a lot.
He was at first, he was warning us the dangers of AI. And now he’s saying we need to integrate with AI. In order to not let us out, compete us.
Right. So we have to all you see, I ultimately believe when people come up with these ideas, one has to always question that person and their surroundings. Well, Elon Musk grew up in South Africa, where 97% of whites control 3% I’m sorry, 97 3% of whites control 97% of blacks.
Everyone wants to do well, well, I’m just saying you can’t not. He was part of the elite there. His family profited from exploitation of lots and lots of people.
So does that affect his thinking? I don’t know. But I can’t as a as an untouchable from India. I know that is affected me.
So it would be so for his thinking. itself is, comes from a notion of human beings are commodities, and they exist for maximization of someone else’s capital. So in South Africa, a lot of blood diamonds, a lot of people mining all sorts of rare earth mineral minerals.
Well, you know, a lot of and by the way, they also there were a lot of black people and not a lot a black strada, who also took advantage of their majority right now what’s going on in South Africa was they never had a good revolution. They basically promoted numbskulls, like Mandela, who was basically a sellout and a whole nother discussion we can have, you know, I met people who were in South African prisons, blacks and whites, okay. Mandela was considered a, basically not that smart of a guy.
The whites in South Africa promoted him just like they promoted Gandhi in India, to quell a real powerful revolutionary movement. And, and so they had this basically, a bunch of white people left and a bunch of black bourgeois came in. And that’s why you have all the corruption there.
They never address the fundamental issues in South Africa of exploitation. Okay, so now they want to create the black white thing when it was really about massive exploitation of labor. That’s what happened.
So now Elon Musk comes from that background. So his view on labor, and the essence of being human is driven by oh, we’re all going to be cyborgs. Well, that’s a choice we have.
And this is something started talks about too. At any point, each one of us has a choice, even our emotional decisions we make. So they want to remove there’s no choice.
Elon Musk says this. Yeah. Okay.
What any point we as humanity have a choice, and that’s something they want to remove. Because that’s part of being a human being to recognize that you do have choice in any instant, and they kind of want to ease you into the by getting you addicted to your phone. Getting get Yeah, so you know, Marx talked about that.
The goal, the goal would be we would fight against the machines, we would separate ourselves when in fact, what’s happened is, we’ve become so close to our machines. I mean, you probably that’s the first thing you look at when you wake up as well when you like everyone. skill to somebody.
Yeah, I think a lot of people now do, right. So we become so integrated to. So the issue is will these machines then just become part of us? And to me, this is where the deep, deeply spiritual question comes, you see in the Hindi, or the Indian or the Hindu spiritual tradition, there is a concept of OSHA’s Ko shhs, where they believe that there’s the essence of who you are, and layered on top of that word, these different bodies.
So there’s your soul body, then there’s your etheric body. There’s your causal body, the astral body, and then the physical body. Okay? So if you remove the physical body, you have the astral body, you remove the astral body, there’s a causal body, you move the etheric body, you come to the soul body, which is who you truly are your true state of consciousness.
When all these layers have been removed, the causal body represents your past. I mean, there, I mean, I’m not gonna we can do a whole podcast on that. But the notion was that who you are, is something you don’t even know who you are, until you go inside yourself, and you remove yourself from the illusion of what you think you are.
Now, here’s the interesting thing. And I’ve thought about this. So if you believe that concept, we have these different sheet sheets on ourselves, like different clothing we wear.
Yeah, and most human beings live in the physical body. Now, there are certain spiritual traditions which believe you can peel away those layers, and you can understand who you are in its essence, okay. Different than what starcher proposed.
Okay. But here’s the idea. Suppose one day, Richard, and you may want to think about this.
Let me pose to you that one day. Not in the too far distant future, I give you a set of contact lenses. All right, where it is no different than your screen on your iPhone.
But 3d. Yeah, which we can do the Oculus. But imagine it’s just embedded from the time a child is born, it’s embedded onto that child’s eyeball.
And I give you hearing, or it’s injected in the fetus, right? Yeah, so some, but we know we can definitely probably put contact lenses on. And I give you hearing that you can hear things, not only locally, but you can tune it to anywhere in the world. Okay.
And then I have a hologram of you, which we can do holograms, and they’re getting better and better and project you. So now, Richard, I’m talking to you, and I’m wearing this gear and you’re wearing the skirt. Marcel, and you are Richard, how do you know you’re physically here.
And let’s say you grew up with this year over year over year, generation over generation. And this became the norm, just like the internet is going to fade into the background. This too, would fit, you’d forget about it.
Yeah, this would become I could be a million years, or a million light years away. But this room would feel real, because by the way, they have haptic sensors where I can feel physical things. So you would be you and I would be in this room.
You could be a holographic image that gets better and better and better fidelity and computing gets better. And we would be in this room. This is a real phenomenon.
I could touch your sweater here feel it because I have haptic sensors. So here’s an interesting question. How do we know this hasn’t already happened? Yeah, it’s exactly what I was that we are because that’s what the Indian mystics argue, the Indian mystics argue the purpose of existence to understand who you are and see things as they truly are.
So in Buddha’s one of his last great lectures, he gave two lectures on what’s called the Parthenon on apana meditation, which he said the idea is for you to see things as they are, and there’s a meditative methodology he gave. And what he said you would find is that everything is impermanent, that you are covered with the sheets, and this is an illusion. What we see, so what, let’s say we did that.
Okay. And then someone else did that. On top of that it could this could keep going on.
Yeah. And it makes me think maybe, at some point in time, we only had one sheet that was really Joshi and maybe that’s how most people interacted and with perma spirit. Yeah.
So And over time, all these different disturbances, governments, whatever people say, well, they created different sheets, right, created different sheets, which just became the norm, which people born nowadays they’re kind of in a way like right away born with all these sheets are getting sheets over time. That’s why we always like think of children the way they think it’s like, amazes us sometimes of like, what they say or like, right, how they think about certain things, right? Yeah. So ultimately, that’s what this is about.
The people who are writing a lot of stuff about AI a lot of these people don’t even know what AI is, I think in this podcast, you guys have really learned that it’s basically statistics and it’s trying to take a minute a mechanical perspective. The other piece of it An AI here that needs to be understood is that it is really an opportunity to ask this deeper question as we’re doing right here. What does it mean to be a human being? And because, and what choices are we going to make? Yeah, are these choices that we’re making? Or is Elon Musk making it, it was basically concerned about being a billionaire.
And leaving some legacy behind makes a lot of money off government subsidies turn into like a religion, right? Like he basically wants to upload his brain into like a machine and wherever you know, that he lives forever. That’s kind of a thought. And if they upload their consciousness until like this AI, they’ll be able to live forever, and they’ll be gods.
It’s kind of dangerous, in my opinion. Yeah. Why do you think it’s dangerous? Richard? As we were talking earlier, just the population, what happens when AI becomes too powerful? And they want to start getting rid of humans as you talk? Yes.
So let’s talk about deep. So if you go down if you take capitalism, to its full conclusion, right? revenue minus expenses equals net income. Okay, so let’s say we’re in this phase one of AI deployment, phase one looks like this, transfer as much human knowledge and skills to silicon based machinery.
Okay, so phase one ends where pretty much none of us need to work. Meaning you have machines doing pretty much everything mowing your lawn, you know, being your plumber. I mean, you could take this to anything, right? Yeah, handling it pretty much anything you need.
So now, you have a bunch of humans. By the way, you know, we have three economies right now we have 1/3 of people in the United States do not work at all, they don’t work. Another third living the gig economy, which is they do a gig contractors like an Uber driver.
And the third third actually still does work professionals. Yeah. And those three economies are separating at lightspeed by the way, so AI would support a, the, definitely more and more people would not need to work.
I mean, you could outsource the teacher, you could start outsourcing a lot of stuff. So you could then argue in face to this world of AI, in a in a in a reason, if it was a quote unquote, good model, you work maybe one day in a week to do creative stuff, or something that society needs. Okay.
And the other six days, you get to hang out and just be creative. To the only people needed it would be the ones the people working in it in AI and met and mainly AI making the AI better and that kind of and eventually, AI would self itself manage its AI, see that machines managing machines, so that but somewhere, let’s say that happens right? Now, those in power, who actually own all of this machinery to do the AI are gonna say shit, why do I need all these human beings are just like not doing anything? That’s what the way they would see it. That if it hasn’t already taken over? Well, yeah.
Or if the AI hasn’t taken over, or to your point, those people become part of AI because they become cyborgs. Right. Yeah.
So they become machine like, and then yeah, so you have the complete fusion of the silicon creatures, with the previously carbon creatures who have enough money to become silicon creatures, right? Because as you said, a guy these guys, billionaires would say, I’m gonna live forever, I’m going to basically transfer my consciousness and I’m going to control all the means of production. Like so Amazon, Jeff Bezos, basically, his brain is in Amazon, okay. Or he may have choices.
People may have choices, I may transfer my body into a physical body or other bodies. I mean, there’s all sorts of combinations of this. Yeah.
So they’re going to wake up and say, Well, why do I need all these other 7 billion people? Everything’s been done. I mean, I got my farming done. I got this done.
I got all these machines. Why do I need these people? That’s the question that’s going to come up. Oh, the pollution is getting worse.
We have too many, like, we’d like to depopulate. And it’s more likely to happen if we, if humans actually do integrate with robots and become cyborgs. Because the robotic like way of thinking would be like, yeah, like you’re saying they’re useless.
They’re polluting the humans are polluting the earth. So let’s kill the rest of the ones that aren’t cyborgs. Or just kill most of them.
So Right. So more for me. Yeah.
Right. So if you look at what’s going on with countries like Africa, China, very wealthy billionaires, and I have some guys that I know are very wealthy billionaires, and I’ve been watching over the last 1020 years, they’ve all been going to Africa. You know, under some claim that they want to help the darkies air with malaria, or they want to help them with their water problems or help them with their food.
But what if you follow what they’re doing? Many of them set up nonprofits and are buying huge tracts of land. Chad resources. Yeah, all.
So Africa is like the next big wilderness. Yeah, for them to own for themselves and their kids. And they know that and they know that.
So it’s like the next West, right? Think about a massive amount of resources, beautiful lands, etc. So you have to really start wondering what these people’s goal is, what is their intelligence? Where do they want to take this. And I think the purpose of this podcast is to really start empowering you to realize that at any point, we as humans have choice.
We can choose to what technologies we want to implement what technologies we don’t want to. And the military industrial, academic complex, works together for its own existence. So military to words, and we call the industrial complexes, these massive high tech companies, and academia.
Academia has essentially a bunch of very narcissistic, frankly, a lot of robots, who are not so smart. But we’re robots, who served the military industrial complex to preach to others upgrade all of this is going to be for themselves, frankly, and mostly view the world in a reductionist way, in a riddle in a reductionist way, in a way that profits and military industrial academic complex. Remember, most of these academics, what’s happened in academia has become robotized.
The old days of academia giving tenure was that it would be a place for free speech, and you’d get to talk a lot of stuff. But now, in the seven years from the time you’d become wanting to be an academic, they get rid of all the riffraff they get rid of all the rabble rouser, we end up with a bunch of robots in academia itself. So we’re basically creating robots.
So I think that’s what we really want to really leave with is you the future of you. depends on the choice and recognizing that you have choice. You know, what choices you make for you, and ensuring that at any point, are you getting more truth? Are you getting more freedom? Are you getting access to control your own health? And if those three questions are no, your area heading towards to this world of massive exploitation? It almost seems like if the whole system’s going down that path, there’s is there? I mean, maybe you could change it for your family.
But for humanity, can you change it? Because I mean, going back to modifying your kid? Seems like all parents are gonna want to do that. I don’t know. I was gonna ask you this.
Like, if you had a kid, what do you actually what do you do that? Like? This is not a genetic engineering? Yeah. Because not like, I was looking at this stat of how many kids, young Americans are on Ritalin. And it’s 4.
5 million, which is like, it just keeps on going up. And yeah, and for parents, it’s very easy for them to not think of the side effects and just give it to them just so they can compete with the other children, they think they’re competing. And I feel like that’s if that’s what the future is going to be like, you can modify your kid you can make him have a 300 IQ, the high low IQ, so why not do it all the other parents are doing with are doing it and if I don’t do it, my kids not going to be able to compete with the other kids who got in there be able to get a good job.
So everyone’s gonna do it. Like we see that pattern with Adderall and with Big Pharma. Is there anything we can do? I mean, is do you see the future? Well, I guess it comes down to what the goal is, right? Yeah.
So there’s a there’s a paper that just came out written by the scientists who looked at 257 hunter gatherer societies by the way hunter gatherer would they call the quote unquote the noble savage it’s funny there’s a yesterday’s watching his show on the Hudson people ha de Za, who are still the last remaining hunter gatherer societies one of the few that remain like this and that all these people from these elite institution go study was hilarious to watch because these people are just looking at them thinking they’re a bunch of idiots and the way these people are talking about them in the documentary makes them look like a bunch of morons. It’s just the way they’re talking about a they know better and just the way they’re talking this high level intellectual nonsense. My point is this, that hunter gatherer societies with this one if you looked at their health, they had the some of the key they were getting higher macronutrients higher micronutrients, low, you know, they ate low glycemic index foods.
They had a higher base, alkaline base, they had their bodies were more alkaline. I mean, you go to others about it was a multi system model of health, none of which we follow right now. So we have moved the whole area of health into manufactured products, the stuff that we put in their mouth, they ate very little salt, right? It’s a completely different world.
And all this is came from the time when we moved away from a hunter gatherer model when we employ technology, to domesticate animals when we created this, they lived a very rough life, probably. But they had community and other things that we don’t potentially have now, they could have been happier in general, they could have been happier. So the issue is, what is the goal? Is a goal maximization of profit? Or is a goal maximization of something else you call happiness? Yeah.
So what is what are we trying to maximize? So that’s really the fundamental question. And that’s the struggle you have with technology. And the medical field, right is like trying to actually help people and not getting caught up and destroyed.
Right, right. So what I’m saying is, so this leads to the farther question is, we think human beings know everything. So this comes down to the thing of the concept of, as we talked about, in the last podcast, the concept of a human centric worldview.
So we have this notion that we know everything that may be forgetting and getting into the spiritual thing, just from the mass of the universe, how big it is, how billions of years it’s trillions, it’s exactly, let’s say billions, right? Our existence is like a little, small, little, probably 100, you know, a single frame in a movie that goes on for millions of years. So are we saying that we know how the natural laws that created us, we know how to manipulate those laws better than nature? Is it? This is the outstanding question, are we a part of something that goes we’re we’re not the creator of everything that we think that’s around us? Maybe? The it’s a systems way, we should look at life, there’s an interaction between us, the rock, you know, the land and the animals around us? And that our thoughts even come from that? Right. So So where does this understanding come from? And I think this is the open question.
And who’s deciding all of this? For example, do we even know the origin of where human beings came from? Well, the way the academic model works, you get a, you get to win a Nobel Prize, the first one who writes it? Well, how do we even know what they’re writing is true anymore? Yeah. Because if the motivation is to get federal funding, and you get more grants, how do we even know their ideas of what they’re writing is even true? Yeah, there’s on the soft areas like anthropology and sociology, etc. Because the motivation is a Harvard is the expert in the origin of human species? Well, we don’t even know, there’s a lot of evidence that says there’s like lost civilizations, and that civilizations have commented on Ely, people, academics, like get really mad when you bring that up.
Right, right. That may be human existence, like everything else itself is a cycle. Maybe humans existed, we hit a peak point, we destroyed ourselves.
And we went back and this has been going on. And it’s not a exponential curve that just keeps like this guy, Ray, one of the guys who’s a Google CTO says, oh, you know, we’re gonna hit synchronicity and red hurts. Well, yeah, Ray Kurzweil.
So this is a very human centric concept, that we will keep growing and growing and growing us and expanding. What well, maybe there’s a limit to at a point where we destroy ourselves. And this is some scientists, that maybe the human brain is itself too clever for itself.
That maybe the real cleverness comes in saying, Oh, we shouldn’t go beyond that. Yeah, you’d like we should not develop those things. Maybe that’s the real intelligence and the ones that keep developing and growing, they do something that’s against nature, natural law, and they just destroy themselves and they keeps going to the cycle, maybe.
Maybe the cultures that eventually do meet one another, are the ones who found this range of existence where you actually make a decision. No, we’re not going to do that. Yeah, like, yeah, we can do that.
But we’re not going to do that. Right. Like they actually know where to stop.
Yeah, I was thinking, I don’t know what you said. I’m saying that was maybe that is a technological, like a hyper true intelligence. Well, that’s what would make us true humans is actually thinking about that and actually doing something to stop it.
Right. Because I was thinking about it in terms of like GMOs as well. It’s like, we know, we can modify the food, it’s going to be cheaper.
But we also know that there’s gonna be some side effects, like not good effects for humans. And I was thinking about that in terms of like AI as well. It’s You can modify yourself to have 400 500 IQ maybe in the future.
But is that really smart? Like? What’s that going to lead to? Is there going to be like a default or something like? Or what that? Well, the issue is, what does it mean to be a human? Yeah. And I think we need to really think about this. What does it mean to be human, and I have a much more open understanding of it.
And we cannot have that. I’m saying my end point is we don’t even know what it means to be human. Forget freakin AI.
We don’t even know what it means to be human. Because from the instant we are born, we are constrained. So the only way way out of this is that we need to remove all those barriers which stopped us from being human.
Which is a military industrial, academic complex. Because the military industrial academic complex, is the cyborg, which exists for its own perpetual growth. And it’s the one that needs controllability and observability, or the inner voice inside of you that says, You can’t do this, right, which is maybe like society or your parents or everything.
But it’s clear that human beings do well, when they have a sense of community. Yeah, in terms of their own health, physically, mentally, emotionally. I mean, the number of psychologists I think over the last 20 years, there’s to be like, 4000 men or marriage counselors.
Now there’s 40,000. And you have divorce, which is, you know, when maybe marriage is changing, and all those relationships are changing, that’s one view. But my point is, it’s not like therapists have helped humanity come together.
Right, it’s ultimately going to be the bonds of the interconnections that people develop inside their communities. So what that what the system wants you to do, it wants you to develop interconnections to it. Yeah, he wants you to be connected to it versus another human being, who wants the government to be your parents in a way that your mommy for them? You know, so because that’s where profit comes from.
So the issue is you take is, I think this is a central decision that people should make, and people should start thinking about, because, and who is making those decisions for them? You know, this is why I think it’s really going to come down to this fundamental issue of centralization, of whatever you want to call it, an intelligence versus decentralization. Yeah. You know, if you think it’s going to be invented, inevitable that we’re going to be merging into machines.
Well, who’s making that decision? You know, yeah, that’s cuz, yeah, I mean, for me personally, at first, that’s what I thought. And then actually, like doing a bit more research and actually thinking about it more. I changed my mind, I was like, Is this really a good path for humanity? And ultimately, it doesn’t look like it is.
Yeah, it’s gonna be a good path. It again, it comes down. And we talked about in the previous button, what is the goal here? What is the goal? And that’s what we got to think about.
And so fundamentally, artificial intelligence doesn’t think about the goals, human intelligence, what makes a human being a human is they’re actually sitting and reflecting and thinking about what is the goal. And if you don’t think about what is a goal, you’re probably more than likely already bowing down, and you’re probably AI yourself. That’s what I think we’d like to end with think about, Are you a human? Or are you a robot? And whether if you’re a robot, it’s going to be easy to put you into a silicone body? And you may want that, and then what does it mean to be human? And what does it mean to evolve? You know, is there evolution? Or do we have choice? These are some more of the deeper questions that we need to ask, and who is making these decisions for you? Are you making these decisions? Are these decisions already being made for you? If we want to combat this in a way we have served being a bigger part of our community, right, would you say? Yeah, that’s why I think, you know, I think the entire emphasis people should start recognizing is that there’s something to be said about a recognizing that there is a set of robots, who wants to centralize power and they, and they thrive on centralization of power? Like they know it all.
And I want to call them robots. Actually, I would say that we already have a bunch of carbon based robots running around, because some of them are conscious of the fact that they’re doing that right. It’s yeah, they’re actually conscious and airy and people on top.
Yeah. And they understand systems and they know what they’re doing. You know, so for example, you know, I live in this neighborhood here, you know, you know, it’s, it’s an interesting neighborhood in the 40s.
There was a guy I called Carl Koch. And another guy called Walter Gropius are part of the Bauhaus movement, who very interesting artistic movement. And they built these homes here for 5000 bucks at a time in the 40s when people were building these huge ornate homes, and they went built simple homes, which were affordable, recite recycled material, which was unheard of in the 40s, when Carl Koch was almost kicked out of Cambridge for his avant garde ideas.
This was an artist community of people very different I would call human beings. Well, now I look around me, there’s a bunch of robots who live here. You have one guy down, there was an MIT professor who was a robot, another MIT professor over here, a robot, you know, guys, a hedge fund manager over there as another robot, another guy next door, who’s who’s a robot who just lives off his family money.
You know, a bunch of robots live around me. And it gets interesting. When I put my sign down at the bottom, I held Shiva for Senate.
Right? Uh, one of the robots came to my door. And he said, Oh, he wanted, I didn’t want to talk to him, because I knew why he was coming. Well, several years, he’s put up his another robot sign there was a Democrat who ran my I was running as an independent.
And he called up all the other robots to try to convince him my sign should get taken down. And it wasn’t even and we didn’t even have real Indian fake Indian. Now what what why was that? Because it concept, I was a new guy, and maybe to Indian guy, dark skinned Indian guy, and the only darky up here who’s running that I’m running as an independent.
I’m attacking their friend, Elizabeth Warren. All of these didn’t compute for them. The very thing is, you’re not in a box.
I’m not in a box. Right. And my point is that there are, I would argue that we already live in a world of AI.
And so it is going to come to a battle of machines versus robots. And we’re already seeing that set of robots who say on left, you’re right. Yeah, I’m I am.
I’m this or that. And I think the real question is, are the real insight, I think that we can leave this podcast with is, we already live in artificial intelligence, people who are simply taking an input and putting out an output. I’m a Democrat, Elizabeth Warren is for the common man.
Right? Donald Trump is a racist, whatever you want to go down, whatever the flavor of the month is, right? The Republicans are like this. Democrats are like this, right? Splitting people up into little buckets. Right? And that segregation of people, oh, if you’re a an engineer, you must be like this.
If you’re a journalist, you must be like this, separating people out and then controlling them. And I would argue that the real issue is people want centralization of power on decentralization, more fundamentally, that people are human fighting robots. Yeah, people were thinking input output.
i That’s really the bottom line it is, and we can change it. That’s another we can change it too late. By breaking from that by saying, You know what, I’m not going to listen to 32nd ads on TV anymore.
I’m not going to vote for a Democrat or Republican vote for this two party system. That’s part of becoming a human being I purposely stopped watching commercials years back, I would just like to now or turn it off completely. And then when the show came back on, I would turn right.
I am going to, on my own, trying to understand how systems work. Yeah, I am not going to listen to left or right. I think this is probably the biggest thing, someone, this is a decision.
Look, one of the biggest things we have in human history right now that never existed before, is human suffrage. I mean, nearly everyone in the world can vote now. Now, I’m not saying who you can vote for as good.
But there was a time when we were just slaves. There were a time when we were serfs. Yeah, but this has occurred.
And this is an interesting opportunity. Yeah, it really is. Right.
So those in power, they go one step forward, and they try to put us two steps back. So they created a two party system, to try to force us back into the machine. And one of the most important things people can do is to recognize that they don’t owe anything to this two party system that they need to move beyond left or right.
And to recognize that we’ve been given an amazing opportunity to actually choose how we want to live our lives exactly like these, like this time period when we have the freedom to go on the internet to think by ourselves. We’re not slaves anymore. But we’re still coming into this time of age where everyone’s addicted to their phones and when it seems like people are becoming more slaves by and made by being addicted to their phones and me becoming cyborgs.
So we should make A is servant to us, which means the robots, human or other, quote unquote, in carbon form should be servants to humanity. So for example, you can argue that you had technology. So, for example, we got rid of travel agents per se, right? But now, if you were a travel agent before, now you can go do things online, well, maybe you could be a real travel agent, like really recommend to people, what’s healthy for them where they should vacation, you see what I’m saying? Like be a true adviser.
One of my good friends at IBM had an interesting insight, he said, there will be two waves of the internet, the first wave would be everything would get automated. But it goes a second wave is going to be more interesting where you people want full service you which means humanity. Yeah.
So that’s the way we can choose. Look, everything to get automated. So fine.
That’s great. But now the second wave could be how do you use technology? To bring out your humanity? Yeah. Right.
So you could say, Look why I got all this time, I can find all these amazing places on the internet to travel to. But what do I really want to do? Yeah, where do I really want to go? Right? And same way, hey, wow, I can learn a lot of skills. No, hey, I want to learn pottery.
I want to learn programming. I want to learn so you can now become truly a polymath, whatever, you’re not be a jack of all trades or a master of none. Not that dichotomy, right? Not a specialist, but you could actually learn a lot of stuff.
Yeah. So I think that’s and the issue is, are we as humans going to make those decisions and be truly human? Or are we going to be essentially end up being robots and be a slave to robots? Because that’s really the question. Are we going to be a slave to robots? Which is centralized government, a bunch of people who want to maximize profit all day? Are we going to be humans and they can be a slave to us? Or that methodology, you say? Seems like it’s the most important time it’s the most important time.
It’s really the truly the notion of independence and freedom, and truth. And I think it’s gonna be a decision. Do you want to be ruled by robots who already exists? They’re in carbon based creatures, right? Politicians, and the military, industrial academic.
That is a robot. Yeah, that is the cyborg already. It’s already here.
It’s been here since the 1940s. Or do you want to be a human being? And do you want to declare your independence, you want to declare your humanity and preserve humanity and preserve humanity? Yep. Anyway, thank you.
So I think this was a good discussion. Yeah, definitely. All right, thank you, we have allowed our country to be taken over from within.
And the end goal is you will have a homogenized world where we will become slaves. Because there is a condition among the elites that really thinks they’re better than you, deep down inside them, that you don’t deserve the freedoms you have. They don’t this reality is what people need to wake up to.
And we need to all unite working people. There’s only one movement that can do that. And that is the movement that we started creating here messages, the movement for truth, freedom.
And I’ve been a student of politics since I was a four year old kid setting revolutionary movements, left wing right wing, there’s a physics there’s a nuclear science to destroying the establishment. To build a bridge, you need to understand Newton’s equation, you need to understand the laws of gravity, you need to understand Poissons ratio, there is a way to build a revolution. And that’s why I put this together.
My goal is to train a army of truth, freedom and health leaders. We don’t need followers on social media, we need leaders, but they need training because the educational system does not teach them history, nothing. So three hours is what I’ve started doing.
That’s the solution. We got to train people first with understanding what a system is the dynamics of all systems in nature. The second is understanding the interconnection between truth, freedom and health.
Freedom is the ability to move freely, communicate freely, talk freely, without freedom, you cannot convert ideas, hypothesis into truth, which is science. And without freedom, you can’t really get to truth and without truth, you make up fake problems and fake solutions, which means you destroy our health, and without health, which is the infrastructure of us and our body. You can’t fight for freedom, truth, Freedom help.
Third concept is it has to be bottoms up working people, people who work uniting. And what the right wing has done is whatever you say working people unite, that must be communist. Meanwhile, they’ve let them Democrats run unions, which suppress workers completely corrupt.
But when you look at the arc of American history, it’s been when working people came up, we need to go local. Every solution I’m coming up with as a part of this movement. We’re giving the science which is a truth and then we tell people what they can do on the ground.
Like with election fraud. You don’t need to wait for some lawyer. Our goal is to train people to go local to go to local to go local fight locally.
Forget lawyers, forget politicians, forget celebrities. You’ve got to learn politics, and there is a science to it. They They lock us down, we should be ready to shut them down.
And the fourth part of this principle is a not so obvious establishment. So when you look at a system, there’s always something that disturbs you from getting to your goal with the biggest disturbances and not so obvious establishment, which are those people who claim there for you on the left and the right, the Al Sharpton to tell black people I’m for you. The Tucker Carlson’s.
Do you think any true anti establishment person will ever be on Fox or CNN? I don’t think so. They both mislead working people back into the establishment. Without this solid understanding of political physics and theory, you’re screwed.
You’re gonna follow the left wing Bernie Sanders. Oh, he said something or Robert Kennedy scumbags? Are you going to follow some right wing talk show host, they’re not going to lead us to liberation. It’s us.
We’re building a bottoms up movement and that political physics, it’s a nuclear science of change. Bottoms up, we have to organize to understand that there is people who talk a good game, and and look at what they actually do left and right. I’m sorry, Sean Hannity may say some good things, but I don’t see the urgency in his voice to get something done.
And it can only come when you weaponize yourself with the right knowledge, you need to be able to identify a rap, you know, Christ and go after the Romans. Right, it was the Pharisees and the Sadducees, who screwed him up his own quote unquote, people. And that’s where we’re at.
So these four concepts are built into a curriculum, and people can go to truth, freedom help.com, and it’s an educational program, we need to train people in political theory, you need to have physics, and I’ve created that curriculum. People need to get educated, we need to get educated fast.
And within half an hour, an hour, I can teach people two years of MIT control systems, I teach people those concepts that I apply it, anyone can understand it. And then you say, Oh, I gotta build a bottoms up movement. They have to get politically astute.
And then they have to go locally and act not sit there on social media, they have to act locally, defy locally, do civil obedience locally, but with knowledge on how to build a movement, and the Senate campaigns expanded to the movement for truth, freedom and health, and they can find it on truth, freedom health.com. So people can sign in, they can get access to a bunch of videos, if they want to take a course and become a truth freedom health leader, I offer a full scholarship there, but we want people to make a commitment that they’ll study that they’ll get certified that they’ll go do activities on the ground.
So go to truth, freedom health.com
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